Could high-tech goggles be the key to spotting secret swimmers?
A couple is expecting their first child after nearly 20 years of trying, thanks to artificial intelligence that found “hidden” sperm in a man who was once thought to be shooting blanks.
The futuristic treatment, currently only available in New York, could offer fresh hope to thousands of men who’ve long been told they’d never father a biological child.
Studies show that male infertility affects about 10% to 15% of US males who are trying to conceive. Christoph Burgstedt – stock.adobe.com
Most healthy semen is teeming with millions of sperm — but up to 15% of infertile men have azoospermia, where there’s virtually nothing to be found.
“A semen sample can appear totally normal, but when you look under the microscope, you discover just a sea of cellular debris, with no sperm visible,” Dr. Zev Williams, director of the Columbia University Fertility Center, said in a press release.
Until now, men with azoospermia had few ways to overcome their missing swimmers.
“The options have typically been either to use donor sperm or to try undergoing a painful surgery where a portion of the testes is actually removed and they look in the testes to try to find sperm,” Williams told Today.com.
Frustrated by the lack of effective solutions, Williams and his team turned their gaze to the galaxies.
Borrowing AI technology used by astrophysicists to detect faraway stars and planets, the researchers spent five years developing the STAR system — short for Sperm Tracking and Recovery — to search for life of a different kind.
And it works: When tested on a sample that embryologists had spent two days combing through with no success, STAR found 44 sperm in just one hour.
Williams and his team were inspired by the high-tech methods that astrophysicists are using in space. Columbia Doctors
“We’re using the same technologies that are used to search for life in the universe to help create new life right here on earth,” Williams said.
In March 2025, a woman using the pseudonym Rosie became the first to get pregnant using the STAR method — after nearly 19 years of trying to conceive with her husband, who has azoospermia.
“There really was nothing else out there,” Rosie, 38, said in an interview with Time magazine. “Especially because I am running quite a few years ahead of where we should be [for fertility]. I’m not that old, but in fertility years — egg-wise — I was reaching my end.”
For her husband, the process was surprisingly simple; all he had to do was provide a sperm sample.
Researchers then scanned the sample with high-powered imaging, capturing more than 8 million images in under an hour. Using AI, they were able to detect three viable sperm cells.
Once spotted, the tiny swimmers were swiftly extracted by a robot, avoiding damage from traditional methods like centrifugation, which spins the sample and can ruin the cells.
The STAR method eliminates the need for other painful, and sometimes risky, procedures to extract sperm. nito – stock.adobe.com
“Imagine searching for a single needle hidden among a thousand haystacks scattered across ten football fields — and finding it in under two hours,” Williams told the Bump. “That’s the level of precision and speed delivered by the STAR system.”
After extraction, the sperm can be used right away for in vitro fertilization or frozen for future attempts.
In Rosie’s case, doctors had her eggs successfully fertilized within two hours of collecting her husband’s sample. A few days later, the embryos were transferred to her uterus.
Now five months pregnant, Rosie says it still feels surreal. “I still wake up in the morning and can’t believe if this is true or not,” she told Time.
The baby is due in December.
A growing number of people are starting their families with assisted reproductive technology. weyo – stock.adobe.com
The STAR system is currently exclusive to Columbia University Fertility Center, where Williams said several other patients are already in the “banking stage.”
He told CNN the full process to find, isolate and freeze sperm costs just under $3,000. The average cost of IVF in the US ranges from $12,400 to $15,000 but can exceed $30,000 when including medications and genetic testing, according to GoodRx.
While the new tech offers hope, some experts are skeptical.
“At face value, this looks promising, but as with any new technology in medicine, especially in reproductive care, we need to follow the data and study it further,” said Robert Brannigan, president-elect of the American Society for Reproductive Medicine, in an interview with the Washington Post.
Scientists are still working to pinpoint the cause, but suspect environmental exposures and lifestyle factors like obesity, poor diet and physical inactivity are playing a role.
As infertility climbs, more couples are turning to assisted reproduction like IVF and the STAR system for a shot at parenthood.
“With our method, many men who were told they had no chance at a biological child now have that chance,” Williams said.
One surprising outcome is that AI might end up making the most critical functions of banking, insurance, and trading, or the creative functions that require human insights, even more valuable.
“What happens is there’s going to be a premium on creativity and judgment that goes into the process,” said John Kain, who is head of market development efforts in financial services for AWS, in an interview with ZDNET via Zoom.
By process, he meant those areas that are most advanced, and presumably hardest to automate, such as a bank’s risk calculations.
Amazon AWS
“So much of what’s undifferentiated will be automated,” said Kaine. “But what that means is what actually differentiates the business and the ability to serve customers better, whether that’s better understanding products or risk, or coming up with new products, from a financial perspective, the pace of that will just go so much more quickly in the future.”
Amazon formed its financial services unit 10 years ago, the first time the cloud giant took an industry-first approach.
For eight years, Kaine has helped bring the cloud giant’s tools to banks, insurers, and hedge funds. That approach includes both moving workloads to the cloud and implementing AI, including the large language models (LLMs) of generative AI (Gen AI), in his clients’ processes.
“If you look at what we’re trying to do, we’re trying to provide our customers an environment where, from a security, compliance, and governance perspective, we give them a platform that ticks the boxes for everything that’s table stakes for financial services,” said Kaine, “but also gives them the access to the latest technologies, and choice in being able to bring the best patterns to the industry.”
Kaine, who started his career in operations on the trading floor, and worked at firms such as JP Morgan Chase and Nasdaq, had many examples of gains through the automation of financial functions, such as customer service and equity research.
Early use of AWS by financials included things such as back-testing portfolios of investments to predict performance, the kind of workload that is “well-suited to cloud” because it requires computer simulations “to really work well in parallel,” said Kaine.
“That ability to be able to do research much more quickly in AWS meant that investment research firms could quickly see those benefits,” he said. “You’ve seen that repeated across the industry regardless of the firm.”
Taking advantage of the tech
Early implementations of Gen AI are showing many commonalities across firms. “They’ll be repeatable patterns, whether it’s document processing that could show up as mortgage automation with PennyMac, or claims processing with The Travelers Companies.”
Such processes come with an extra degree of sensitivity, Kain said, given the regulated status of finance. “Not only do they have a priority on resilience as well as security, they have evidence that is in a far greater degree than any other industry because the regulations on financial services are typically very prescriptive,” he explained. “There’s a much higher bar in the industry.”
Finance has been an early adopter of an AI-based technology invented at AWS, originally called Zelkova, and that is now more generally referred to as “automated reasoning.” The technology combines machine-learning AI with mathematical proofs to formally validate security measures, such as who has access to resources in a bank.
“It was an effort to allow customers to prove that the security controls they put in place were knowably effective,” said Kain. “That was important for our financial services customers,” including hedge fund Bridgewater and other early adopters.
Now, automated reasoning is also being employed to fix Gen AI.
“You’re seeing that same approach now being taken to improve the performance of large language models, particularly with hallucination reduction,” he said.
To mitigate hallucinations, or “confabulations,” as the errors in Gen AI are more properly known, AWS’s Bedrock platform for running machine learning programs uses retrieval-augmented generation (RAG).
The RAG approach involves connecting an LLM to a source of validated information, such as a database. The source serves as a gold standard to “anchor” the models to limit error.
Once anchored, automated reasoning is applied to “actually allow you to create your own policies that will then give you an extra level of security and detail to make sure that the responses that you’re providing [from the AI model] are accurate.”
The RAG approach, and automated reasoning, are increasingly leading clients in financial services to implement “smaller, domain-specific tasks” in AI that can be connected to a set of specific data, he said.
Financial firms start with Gen AI use cases in surveys of enterprise use, including automating call centers. “From a large language model perspective, there are actually a number of use cases that we’ve seen the industry achieve almost immediate ROI [return on investment],” said Kain. “The foremost is customer interaction, particularly at the call center.”
AWS customers, including Principal Financial, Ally Financial, Rocket Mortgage, and crypto-currency exchange Coinbase, have all exploited Gen AI to “take those [customer] calls, transcribe them in real time, and then provide information to the agents that provide the context of why customers are calling, plus their history, and then guide them [the human call agents] to the right response.”
Coinbase used that approach to automate 64% of support calls, up from 19% two years ago, with the aim of reaching 90% in the future.
Coinbase presents its findings at AWS Summit.
Tiernan Ray/ZDNET
Finding fresh opportunities
Another area where automation is being used is in monitoring alerts, such as fraud warnings. It’s a bit like AI in cybersecurity, where AI handles a flood of signals that would overwhelm a human analyst or investigator.
Fraud alerts and other warnings “generate a large number of false positives,” said Kain, which means a lot of extra work for fraud teams and other financial staff to “spend a good chunk of their day looking at things that aren’t actually fraud.”
Instead, “customers can use large language models to help accelerate the investigation process” by summarizing the alerts, and then create a summary report to be given to the human investigator.
Verafin specializes in anti-money laundering efforts and is an AWS customer using this approach.
“They’ve shown they can save 80% to 90% of the time it takes to investigate an alert,” he said.
Another automation area is “middle office processing,” including customer inquiries to a brokerage for trade confirmation.
One AWS client, brokerage Jefferies & Co., has set up “agentic AI” where the AI model “would actually go through their inbox, saying, this is a request for confirming a price” of a securities trade.
That agent passes the request to another agent to “go out and query a database to get the actual trade price for the customer, and then generate the email” that gets sent to the customer.
“It’s not a huge process, it takes a human, maybe, ten, fifteen minutes to go do it themselves,” said Kain, “but you go from something that was minutes down to seconds through agents.”
The same kinds of applications have been seen in the mortgage and insurance business, he said, and in energy, with Canada’s Total Energy Services confirming contracts.
One of the “most interesting” areas in finance for Gen AI, said Kain, is in investment research.
Hedge fund Bridgewater uses LLMs to “basically take a freeform text [summary] about an investment idea, break that down into nine individual steps, and, for each step, kick off an [AI] agent that would go understand what data was necessary to answer the question, build a dependency map between the various trade-offs within an investment model, and then write the code to pull real-time data from the investment data store, and then generate a report like a first-year investment professional.”
Credit rating giant Moody’s is using agents to automate memos on credit ratings. However, credit ratings are usually for public companies because only these firms must report their financial data by law. Now, Moody’s peer, S&P Global, has been able to extend ratings to private companies by amassing snippets of data here and there.
“There’s an opportunity to leverage large language models to scour what’s publicly available to do credit information on private companies,” said Kain. “That allows the private credit market to have better-anchored information to make private credit decisions.”
These represent “just amazing capabilities,” said Kain of the AI use cases.
Moving into new areas
AI is not yet automating many core functions of banks and other financial firms, such as calculating the most complex risk profiles for securities. But, “I think it’s closer than you think,” said Kain.
“It’s not where we’ve completely moved to trusting the machine to generate, let’s say, trading strategies or risk management approaches,” said Kain.
However, the beginnings of forecasting and analysis are present. Consider the problem of calculating the impact of new US tariffs on the cash flows of companies. That is “happening today as partially an AI function,” he said.
Financial firms “are definitely looking at data at scale, reacting to market movements, and then seeing how they should be updating their positions accordingly,” he explained.
“That ability to ingest data at a global scale is something that I think is so much easier than it was a year ago,” because of Gen AI.
AWS customer Crypto.com, a trading platform for cryptocurrencies, can watch news feeds in 25 different languages using a combination of multiple LLMs.
“They are able to identify which stories are about currencies, and tell if that is a positive or negative signal, and then aggregate that as inputs to their customers,” for trading purposes. As long as two of the three models monitoring the feeds agreed, “they had conviction that there was a signal there” of value.
“So, we’re seeing that use of generative AI to check generative AI, if you will, to provide confidence at scale.”
Those human-centered tasks that remain at the core of banking, insurance, and trading are probably the most valuable in the industry, including the most complex functions, such as creating new derivative products or underwriting initial public offerings.
Those are areas that will enjoy the “premium” for creativity, in Kain’s view. Yet how much longer these tasks remain centered on human creation is an open question.
“I wish I had a crystal ball to say how much of that is truly automatable in the next few years,” said Kain.
“But given the tremendous adoption [of AI], and the ability for us to process data so much more effectively than even just two, three years ago, it’s an exciting time to see where this will all end up.”
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A consulting firm found that tech companies are “strategically overpaying” recruits with AI experience.
They found firms pay premiums of up to $200,000 for data scientists with machine learning skills.
The report also tracked a rise in bonuses for lower-level software engineers and analysts.
The AI talent bidding war is heating up, and the data scientists and software engineers behind the tech are benefiting from being caught in the middle.
Many tech companies are “strategically overpaying” recruits with AI experience, shelling out premiums of up to $200,000 for some roles with machine learning skills, J. Thelander Consulting, a compensation data and consulting firm for the private capital market, found in a recent report.
The report, compiled from a compensation analysis of roles across 153 companies, showed that data scientists and analysts with machine learning skills tend to receive a higher premium than software engineers with the same skills. However, the consulting firm also tracked a rise in bonuses for lower-level software engineers and analysts.
The payouts are a big bet, especially among startups. About half of the surveyed companies paying premiums for employees with AI skills had no revenue in the past year, and a majority (71%) had no profit.
Smaller firms need to stand out and be competitive among Big Tech giants — a likely driver behind the pricey recruitment tactic, a spokesperson for the consulting firm told Business Insider.
But while the J. Thelander Consulting report focused on smaller firms, some Big Tech companies have also recently made headlines for their sky-high recruitment incentives.
Meta was in the spotlight last month after Sam Altman, CEO of OpenAI, said the social media giant had tried to poach his best employees with $100 million signing bonuses.
While Business Insider previously reported that Altman later quipped that none of his “best people” had been enticed by the deal, Meta’s chief technology officer, Andrew Bosworth, said in an interview with CNBC that Altman “neglected to mention that he’s countering those offers.”
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